Article

Journal of Coastal Conservation

, Volume 15, Issue 3, pp 337-351

Functional autoregressive forecasting of long-term seabed evolution

  • Serge GuillasAffiliated withDepartment of Statistical Science and Aon Benfield UCL Hazard Research Centre, UCL Email author 
  • , Anna BakareAffiliated withDepartment of Civil, Environmental and Geomatic Engineering, UCL
  • , Jeremy MorleyAffiliated withDepartment of Civil, Environmental and Geomatic Engineering, UCL
  • , Richard SimonsAffiliated withDepartment of Civil, Environmental and Geomatic Engineering, UCL

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Abstract

There is a need for decadal predictions of the seabed evolution, for example to inform resurvey strategies when maintaining navigation channels. The understanding of the physical processes involved in morphological evolution, and the viability of process models to accurately model evolution over these time scales, are currently limited. As a result, statistical approaches are used to supply long-term forecasts. In this paper, we introduce a novel statistical approach for this problem: the autoregressive Hilbertian model (ARH). This model naturally assesses the time evolution of spatially-distributed measurements. We apply the technique to a coastal area in the East Anglian coast over the period 1846 to 2002, and compare with two other statistical methods used recently for seabed prediction: the autoregressive model and the EOF model. We evaluate the performance of the three methods by comparing observations and predictions for 2002. The ARH model enables a reduction of 10% of the root mean squared errors. Finally, we compute the variability in the predictions related to time sampling using the jackknife, a method that uses subsamples to quantify uncertainties.

Keywords

Seabed evolution Forecasting Autoregressive Hilbertian model EOF Jackknife